{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T08:13:19Z","timestamp":1769847199375,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":51,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,5,25]],"date-time":"2025-05-25T00:00:00Z","timestamp":1748131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,5,26]]},"DOI":"10.1145\/3715669.3726785","type":"proceedings-article","created":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T06:57:59Z","timestamp":1748069879000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Learning Disorder Detection Using Eye Tracking: Are Large Language Models Better Than Machine Learning?"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9256-7087","authenticated-orcid":false,"given":"Quoc-Toan","family":"Nguyen","sequence":"first","affiliation":[{"name":"University of Technology Sydney, Sydney, NSW, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7092-3106","authenticated-orcid":false,"given":"Hy","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Deakin University, Burwood, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7922-4040","authenticated-orcid":false,"given":"Quang-Hieu","family":"Tang","sequence":"additional","affiliation":[{"name":"University of Technology of Troyes, Troyes, France"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2546-0043","authenticated-orcid":false,"given":"Tien","family":"Truong","sequence":"additional","affiliation":[{"name":"University of California, Berkeley, Berkeley, California, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5748-099X","authenticated-orcid":false,"given":"Van-Tuan","family":"Pham","sequence":"additional","affiliation":[{"name":"RMIT University, Ho Chi Minh City, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1241-1881","authenticated-orcid":false,"given":"Linh","family":"Le","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Sydney, NSW, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2447-4094","authenticated-orcid":false,"given":"David","family":"Williams-King","sequence":"additional","affiliation":[{"name":"Mila - SAIFH Lab, Montreal, Quebec, Canada"}]}],"member":"320","published-online":{"date-parts":[[2025,5,25]]},"reference":[{"key":"e_1_3_3_1_2_1","doi-asserted-by":"crossref","unstructured":"Yazeed Alkhurayyif and Abdul Rahaman\u00a0Wahab Sait. 2024. Deep learning-driven dyslexia detection model using multi-modality data. PeerJ Computer Science 10 (2024) e2077.","DOI":"10.7717\/peerj-cs.2077"},{"key":"e_1_3_3_1_3_1","unstructured":"Marthe Ballon Andres Algaba and Vincent Ginis. 2025. The Relationship Between Reasoning and Performance in Large Language Models\u2013o3 (mini) Thinks Harder Not Longer. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2502.15631 (2025)."},{"key":"e_1_3_3_1_4_1","first-page":"1877","volume-title":"Advances in Neural Information Processing Systems","author":"Brown Tom","year":"2020","unstructured":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared\u00a0D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, and et\u00a0al. Sastry. 2020. Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems, H.\u00a0Larochelle, M.\u00a0Ranzato, R.\u00a0Hadsell, M.F. Balcan, and H.\u00a0Lin (Eds.), Vol.\u00a033. Curran Associates, Inc., 1877\u20131901."},{"key":"e_1_3_3_1_5_1","doi-asserted-by":"crossref","unstructured":"Erik Brynjolfsson and Tom Mitchell. 2017. What can machine learning do? Workforce implications. Science 358 6370 (2017) 1530\u20131534.","DOI":"10.1126\/science.aap8062"},{"key":"e_1_3_3_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3588015.3588420"},{"key":"e_1_3_3_1_7_1","unstructured":"Leonie Coenen Matthias Gr\u00fcnke Sebastian Becker-Genschow Kirsten Schl\u00fcter Matthias Schulden and Anne Barwasser. 2024. A Systematic Review of Eye-Tracking Technology in Dyslexia Diagnosis.Insights into Learning Disabilities 21 1 (2024) 45\u201365."},{"key":"e_1_3_3_1_8_1","doi-asserted-by":"crossref","unstructured":"Michael\u00a0S Deiner Natalie\u00a0A Deiner Vagelis Hristidis Stephen\u00a0D McLeod Thuy Doan Thomas\u00a0M Lietman and Travis\u00a0C Porco. 2024. Use of large language models to assess the likelihood of epidemics from the content of tweets: infodemiology study. Journal of Medical Internet Research 26 (2024) e49139.","DOI":"10.2196\/49139"},{"key":"e_1_3_3_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3649902.3656490"},{"key":"e_1_3_3_1_10_1","doi-asserted-by":"crossref","unstructured":"Dyke Ferber Georg W\u00f6lflein Isabella\u00a0C Wiest Marta Ligero Srividhya Sainath Narmin Ghaffari\u00a0Laleh Omar\u00a0SM El\u00a0Nahhas Gustav M\u00fcller-Franzes Dirk J\u00e4ger Daniel Truhn et\u00a0al. 2024. In-context learning enables multimodal large language models to classify cancer pathology images. Nature Communications 15 1 (2024) 10104.","DOI":"10.1038\/s41467-024-51465-9"},{"key":"e_1_3_3_1_11_1","unstructured":"Yingqiang Ge Wenyue Hua Kai Mei Juntao Tan Shuyuan Xu Zelong Li Yongfeng Zhang et\u00a0al. 2023. Openagi: When llm meets domain experts. Advances in Neural Information Processing Systems 36 (2023) 5539\u20135568."},{"key":"e_1_3_3_1_12_1","first-page":"1321","volume-title":"International conference on machine learning","author":"Guo Chuan","year":"2017","unstructured":"Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian\u00a0Q Weinberger. 2017. On calibration of modern neural networks. In International conference on machine learning. PMLR, 1321\u20131330."},{"key":"e_1_3_3_1_13_1","doi-asserted-by":"crossref","unstructured":"Jos\u00e9 Hern\u00e1ndez-Orallo. 2017. Evaluation in artificial intelligence: from task-oriented to ability-oriented measurement. Artificial Intelligence Review 48 (2017) 397\u2013447.","DOI":"10.1007\/s10462-016-9505-7"},{"key":"e_1_3_3_1_14_1","doi-asserted-by":"crossref","unstructured":"Steven\u00a0A Hicks Inga Str\u00fcmke Vajira Thambawita Malek Hammou Michael\u00a0A Riegler P\u00e5l Halvorsen and Sravanthi Parasa. 2022. On evaluation metrics for medical applications of artificial intelligence. Scientific reports 12 1 (2022) 5979.","DOI":"10.1038\/s41598-022-09954-8"},{"key":"e_1_3_3_1_15_1","doi-asserted-by":"crossref","unstructured":"Noah Hollmann Samuel M\u00fcller Lennart Purucker Arjun Krishnakumar Max K\u00f6rfer Shi\u00a0Bin Hoo Robin\u00a0Tibor Schirrmeister and Frank Hutter. 2025. Accurate predictions on small data with a tabular foundation model. Nature 637 8045 (2025) 319\u2013326.","DOI":"10.1038\/s41586-024-08328-6"},{"key":"e_1_3_3_1_16_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.35"},{"key":"e_1_3_3_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3675094.3678494"},{"key":"e_1_3_3_1_18_1","doi-asserted-by":"crossref","unstructured":"Lei Huang Weijiang Yu Weitao Ma Weihong Zhong Zhangyin Feng Haotian Wang Qianglong Chen Weihua Peng Xiaocheng Feng Bing Qin et\u00a0al. 2025. A survey on hallucination in large language models: Principles taxonomy challenges and open questions. ACM Transactions on Information Systems 43 2 (2025) 1\u201355.","DOI":"10.1145\/3703155"},{"key":"e_1_3_3_1_19_1","unstructured":"Aaron Hurst Adam Lerer Adam\u00a0P Goucher Adam Perelman Aditya Ramesh Aidan Clark AJ Ostrow Akila Welihinda Alan Hayes Alec Radford et\u00a0al. 2024. Gpt-4o system card. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2410.21276 (2024)."},{"key":"e_1_3_3_1_20_1","unstructured":"iMotions. [n. d.]. SMI RED 250 Remote Eye Tracker. https:\/\/imotions.com\/products\/hardware\/smi-red\/ Accessed: 28 Feb. 2025."},{"key":"e_1_3_3_1_21_1","unstructured":"Aaron Jaech Adam Kalai Adam Lerer Adam Richardson Ahmed El-Kishky Aiden Low Alec Helyar Aleksander Madry Alex Beutel Alex Carney et\u00a0al. 2024. OpenAI o1 system card. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2412.16720 (2024)."},{"key":"e_1_3_3_1_22_1","doi-asserted-by":"crossref","unstructured":"Wihl Jonas et\u00a0al. 2025. Data Extraction from Free-Text Stroke CT Reports Using GPT-4o and Llama-3.3-70B: The Impact of Annotation Guidelines. medRxiv (2025) 2025\u201301.","DOI":"10.1101\/2025.01.22.25320938"},{"key":"e_1_3_3_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3649902.3656494"},{"key":"e_1_3_3_1_24_1","unstructured":"Aixin Liu Bei Feng Bing Xue Bingxuan Wang Bochao Wu Chengda Lu Chenggang Zhao Chengqi Deng Chenyu Zhang Chong Ruan et\u00a0al. 2024. DeepSeek-V3 Technical Report. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2412.19437 (2024)."},{"key":"e_1_3_3_1_25_1","unstructured":"Sijia Liu Yuanshun Yao Jinghan Jia Stephen Casper Nathalie Baracaldo Peter Hase Yuguang Yao Chris\u00a0Yuhao Liu Xiaojun Xu Hang Li et\u00a0al. 2025. Rethinking machine unlearning for large language models. Nature Machine Intelligence (2025) 1\u201314."},{"key":"e_1_3_3_1_26_1","unstructured":"Clare Lyonette Gaby Atfield Beate Baldauf and David Owen. 2019. Research on the educational psychologist workforce. The Institute for Employment Research University of Warwick (2019)."},{"key":"e_1_3_3_1_27_1","doi-asserted-by":"crossref","unstructured":"Patrick\u00a0E McKnight and Julius Najab. 2010. Mann-Whitney U Test. The Corsini encyclopedia of psychology (2010) 1\u20131.","DOI":"10.1002\/9780470479216.corpsy0524"},{"key":"e_1_3_3_1_28_1","unstructured":"Meta. 2025. Llama API Platform. https:\/\/www.llama-api.com\/ Accessed: 19 February 2025."},{"key":"e_1_3_3_1_29_1","volume-title":"Proceedings of the AAAI conference on artificial intelligence","author":"Naeini Mahdi\u00a0Pakdaman","year":"2015","unstructured":"Mahdi\u00a0Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht. 2015. Obtaining well calibrated probabilities using bayesian binning. In Proceedings of the AAAI conference on artificial intelligence."},{"key":"e_1_3_3_1_30_1","volume-title":"Proceedings of the Queer in AI Workshop at the 2025 Conference of the North American Chapter of the Association for Computational Linguistics","author":"Nguyen Quoc-Toan","year":"2025","unstructured":"Quoc-Toan Nguyen, Josh Nguyen, Van-Tuan Pham, and William\u00a0John Teahan. 2025. Leveraging Large Language Models in Detecting Anti-LGBTQIA+ User-generated Texts. In Proceedings of the Queer in AI Workshop at the 2025 Conference of the North American Chapter of the Association for Computational Linguistics."},{"key":"e_1_3_3_1_31_1","doi-asserted-by":"crossref","unstructured":"Thi Kieu\u00a0Chinh Nguyen Duc\u00a0Duy Le Thanh\u00a0Ha Le Thi Cam\u00a0Huong Nguyen and Thi\u00a0Duyen Ngo. 2024. The use of eye tracking in supporting individuals with dyslexia: a review. Disability and Rehabilitation: Assistive Technology (2024) 1\u201316.","DOI":"10.1080\/17483107.2024.2437697"},{"key":"e_1_3_3_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102430"},{"key":"e_1_3_3_1_33_1","volume-title":"CVPR workshops","author":"Nixon Jeremy","year":"2019","unstructured":"Jeremy Nixon, Michael\u00a0W Dusenberry, Linchuan Zhang, Ghassen Jerfel, and Dustin Tran. 2019. Measuring Calibration in Deep Learning.. In CVPR workshops."},{"key":"e_1_3_3_1_34_1","unstructured":"OpenAI. 2025. OpenAI API Platform. https:\/\/platform.openai.com\/ Accessed: 19 February 2025."},{"key":"e_1_3_3_1_35_1","doi-asserted-by":"crossref","unstructured":"Jonathan Peirce Jeremy\u00a0R Gray Sol Simpson Michael MacAskill Richard H\u00f6chenberger Hiroyuki Sogo Erik Kastman and Jonas\u00a0Kristoffer Lindel\u00f8v. 2019. PsychoPy2: Experiments in behavior made easy. Behavior research methods 51 (2019) 195\u2013203.","DOI":"10.3758\/s13428-018-01193-y"},{"key":"e_1_3_3_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/2745555.2746644"},{"key":"e_1_3_3_1_37_1","doi-asserted-by":"crossref","unstructured":"Kaspar Rufibach. 2010. Use of Brier score to assess binary predictions. Journal of clinical epidemiology 63 8 (2010) 938\u2013939.","DOI":"10.1016\/j.jclinepi.2009.11.009"},{"key":"e_1_3_3_1_38_1","doi-asserted-by":"crossref","unstructured":"Andrea Sadusky Emily\u00a0P Berger Andrea\u00a0E Reupert and Nerelie\u00a0C Freeman. 2022. Methods used by psychologists for identifying dyslexia: a systematic review. Dyslexia 28 2 (2022) 132\u2013148.","DOI":"10.1002\/dys.1706"},{"key":"e_1_3_3_1_39_1","doi-asserted-by":"crossref","unstructured":"Thomas Savage John Wang Robert Gallo Abdessalem Boukil Vishwesh Patel Seyed Amir\u00a0Ahmad Safavi-Naini Ali Soroush and Jonathan\u00a0H Chen. 2025. Large language model uncertainty proxies: discrimination and calibration for medical diagnosis and treatment. Journal of the American Medical Informatics Association 32 1 (2025) 139\u2013149.","DOI":"10.1093\/jamia\/ocae254"},{"key":"e_1_3_3_1_40_1","first-page":"34","volume-title":"International Conference on Similarity Search and Applications","author":"Sedmidubsky Jan","year":"2024","unstructured":"Jan Sedmidubsky, Nicol Dostalova, Roman Svaricek, and Wolf Culemann. 2024. ETDD70: Eye-Tracking Dataset for Classification of Dyslexia Using AI-Based Methods. In International Conference on Similarity Search and Applications. Springer, 34\u201348."},{"key":"e_1_3_3_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3379156.3391379"},{"key":"e_1_3_3_1_42_1","doi-asserted-by":"crossref","unstructured":"Nikolay Taran Rotem Gatenyo Emmanuelle Hadjadj Rola Farah and Tzipi Horowitz-Kraus. 2024. Distinct connectivity patterns between perception and attention-related brain networks characterize dyslexia: Machine learning applied to resting-state fMRI. Cortex 181 (2024) 216\u2013232.","DOI":"10.1016\/j.cortex.2024.08.012"},{"key":"e_1_3_3_1_43_1","doi-asserted-by":"crossref","unstructured":"R Vaitheeshwari Chen Chih-Hsuan Chia-Ru Chung Hsuan-Yu Yang Shih-Ching Yeh Eric Hsiao-Kuang Wu and Mukul Kumar. 2024. Dyslexia Analysis and Diagnosis Based on Eye Movement. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2024).","DOI":"10.1109\/TNSRE.2024.3496087"},{"key":"e_1_3_3_1_44_1","unstructured":"Richard\u00a0K Wagner. 2018. Why is it so difficult to diagnose dyslexia and how can we do it better. The Examiner dyslexiaida.org 7 5 (2018)."},{"key":"e_1_3_3_1_45_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.421"},{"key":"e_1_3_3_1_46_1","doi-asserted-by":"crossref","unstructured":"Christopher\u00a0YK Williams Travis Zack Brenda\u00a0Y Miao Madhumita Sushil Michelle Wang Aaron\u00a0E Kornblith and Atul\u00a0J Butte. 2024. Use of a large language model to assess clinical acuity of adults in the emergency department. JAMA Network Open 7 5 (2024) e248895\u2013e248895.","DOI":"10.1001\/jamanetworkopen.2024.8895"},{"key":"e_1_3_3_1_47_1","unstructured":"Liangru Xie Hui Liu Jingying Zeng Xianfeng Tang Yan Han Chen Luo Jing Huang Zhen Li Suhang Wang and Qi He. 2024. A Survey of Calibration Process for Black-Box LLMs. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2412.12767 (2024)."},{"key":"e_1_3_3_1_48_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-naacl.115"},{"key":"e_1_3_3_1_49_1","doi-asserted-by":"crossref","unstructured":"Jia\u00a0Rong Yap Thirishankari Aruthanan and Mellisa Chin. 2025. Artificial Intelligence in Dyslexia Research and Education: A Scoping Review. IEEE Access 13 (2025) 7123\u20137134.","DOI":"10.1109\/ACCESS.2025.3526189"},{"key":"e_1_3_3_1_50_1","first-page":"5191","volume-title":"Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)","author":"Youliang Yuan","year":"2024","unstructured":"Yuan Youliang et\u00a0al. 2024. Does ChatGPT Know That It Does Not Know? Evaluating the Black-Box Calibration of ChatGPT. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). 5191\u20135201."},{"key":"e_1_3_3_1_51_1","doi-asserted-by":"crossref","unstructured":"Yuhong Zhang Qin Li Sujal Nahata Tasnia Jamal Shih-Kuen Cheng Gert Cauwenberghs and Tzyy-Ping Jung. 2024. Integrating Large Language Model EEG and Eye-Tracking for Word-Level Neural State Classification in Reading Comprehension. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2024).","DOI":"10.1109\/TNSRE.2024.3435460"},{"key":"e_1_3_3_1_52_1","doi-asserted-by":"crossref","unstructured":"Andrea Zingoni Juri Taborri and Giuseppe Calabr\u00f2. 2024. A machine learning-based classification model to support university students with dyslexia with personalized tools and strategies. Scientific Reports 14 1 (2024) 273.","DOI":"10.1038\/s41598-023-50879-7"}],"event":{"name":"ETRA '25: 2025 Symposium on Eye Tracking Research and Applications","location":"Tokyo Japan","acronym":"ETRA '25","sponsor":["SIGCHI ACM Special Interest Group on Computer-Human Interaction","SIGGRAPH ACM Special Interest Group on Computer Graphics and Interactive Techniques"]},"container-title":["Proceedings of the 2025 Symposium on Eye Tracking Research and Applications"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3715669.3726785","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:19:13Z","timestamp":1750295953000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3715669.3726785"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,25]]},"references-count":51,"alternative-id":["10.1145\/3715669.3726785","10.1145\/3715669"],"URL":"https:\/\/doi.org\/10.1145\/3715669.3726785","relation":{},"subject":[],"published":{"date-parts":[[2025,5,25]]},"assertion":[{"value":"2025-05-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}